The State of AI in Security Operations 2025 report is here.
.avif)
-min.webp)
It’s almost an understatement to say modern enterprise security teams are drowning in tools. The average large organization deploys between 50-100 different security solutions across their infrastructure, from endpoint detection platforms like CrowdStrike and Microsoft Defender to SIEM systems like Splunk, network monitoring tools, vulnerability scanners, and identity management platforms. According to our latest report, State of AI in Security Operations 2025, organizations deploy an average of 17 alert-generating security tools, with larger enterprises utilizing nearly 30 such platforms. While each tool excels in its specific domain, the collective result is an integration nightmare that's making security teams progressively less effective.
Picture this scenario: A security analyst receives an alert about a potential phishing email that slipped past the secure email gateway (SEG) and was reported by a user. To investigate properly, the analyst pivots between:
Each tool exposes different telemetry, query syntax, and timestamp formats, turning what should be a 10-minute phishing investigation into an hour of context switching and manual correlation.
The scale of this challenge is staggering. Organizations now generate an average of 960 alerts daily, with very large enterprises facing over 3,000 alerts per day. This fragmentation creates critical blind spots where sophisticated attacks span multiple systems, but the connections between them remain invisible when data lives in isolated silos. A sophisticated attack might start with a phishing email, escalate through compromised credentials, move laterally via network protocols, and exfiltrate data through cloud services. No single tool sees the complete picture.
{{ebook-cta}}
The financial impact extends far beyond licensing fees. Organizations spend enormous resources on:
Integration Engineering: Teams dedicate months to building custom connectors and APIs to make tools communicate. These integrations are fragile, breaking with every vendor update.
Training Overhead: Each new tool requires specialized training. Security analysts become generalists across dozens of platforms rather than experts in threat hunting and incident response.
Alert Fatigue: Multiple tools generating overlapping alerts create noise that drowns out genuine threats. Current data reveals that security teams ignore 40% of all alerts on average, with some organizations ignoring over half of their daily alert volume. The consequences are severe: 60% of security teams admit to having ignored an alert that subsequently proved critical, leading to customer data exposure, system downtime, or significant operational interference.
Talent Retention: Skilled security professionals leave organizations frustrated by inefficient toolchains that prevent them from doing meaningful security work. The top challenge facing SOC teams today is that triage and investigation takes too long (cited by 36% of security leaders), followed by gaps in 24/7 coverage (32%) and analyst burnout (31%).
Response times reveal the depth of this operational crisis. The average alert dwell time—the period from when an alert fires to when it's acknowledged—spans 56 minutes, while Mean Time to Investigate (MTTI) averages 70 minutes. These timeframes are catastrophically slow by today’s standards where attackers can extract sensitive information in as little as 48 minutes according to the Crowdstrike Global Threat Report.
Security teams recognize this unsustainable pace. To cope with overwhelming alert volumes, 57% of organizations deliberately suppress detection rules, consciously accepting increased risk to manage operational limitations. When asked what detections they would enable given more resources, security leaders prioritize Cloud security (65%) and Identity security (61%), highlighting critical visibility gaps in two of the most dynamic attack surfaces.
Security Orchestration, Automation, and Response (SOAR) platforms emerged to address these challenges, promising to connect disparate tools through playbooks and workflows. While SOAR solutions provide value for routine tasks, they fall short of solving the fundamental integration problem.
Traditional SOAR platforms require extensive upfront configuration, rigid playbook definitions, and constant maintenance as tools and environments evolve. They excel at automating known processes but struggle with the dynamic, investigative work that defines modern security operations. When faced with novel attack patterns or complex multi-stage incidents, these systems often hand control back to human analysts—who are right back where they started, juggling multiple tools and interfaces.
Artificial Intelligence offers a fundamentally different approach to security tool integration. The recognition of this potential is evident: AI for security has rapidly ascended to become a top-three priority for security leaders (cited by 33% of respondents), ranking alongside data security and cloud security. Currently, 55% of organizations use AI in some capacity for alert triage and investigation.
An AI SOC orchestrator understands the context and capabilities of each connected security tool, automatically determining which systems to query based on the nature of an investigation. When analyzing a potential data exfiltration event, it might simultaneously pull endpoint telemetry from EDR platforms, network flow data from monitoring tools, and user behavior analytics from identity systems, correlating findings in real-time without human intervention.
The key differentiator lies in adaptive intelligence. While traditional integration approaches follow predetermined paths, AI SOC orchestrators learn from each investigation, improving their ability to navigate complex tool ecosystems. They understand that investigating a malware alert requires different data sources than analyzing insider threat indicators, and they adjust their approach accordingly.
Security leaders identify Alert Triage and Investigation (67%), Detection Engineering and Tuning (65%), and Threat Hunting (64%) as the top three use cases where AI delivers the most value in SOC operations. This intelligence extends to data normalization and correlation. Security tools often use different naming conventions, timestamps, and data structures for similar concepts. An AI SOC orchestrator automatically translates between these formats, creating unified views of security events that span multiple platforms.
Organizations implementing AI SOC orchestration report dramatic improvements in investigation efficiency. What previously required analysts to manually query 5-10 different tools can now be accomplished through a single, intelligent interface. The performance gains are measurable: organizations report MTTI improvements from over 25 minutes down to 3-4 minutes, while investigations that previously took SOC teams an ave hours can be completed to greater depth in just 9 minutes.
Security leaders measure AI SOC tool effectiveness primarily through improvements in MTTI (42%), MTTR (41%), and 24/7 coverage (40%). Perhaps more importantly, AI SOC orchestration transforms the analyst experience. Instead of spending their time on tool navigation and data formatting, security professionals can focus on high-value activities like threat hunting, strategic planning, and security architecture improvements.
The momentum behind AI-driven security operations is undeniable. Among organizations not currently using AI SOC solutions, 60% plan to evaluate such solutions within the next year, while 28% are already in evaluation phases. Security leaders collectively believe that AI will handle approximately 60% of SOC workloads within the next three years, representing a fundamental reshaping of security operations.
This transformation addresses the structural challenges that have plagued security teams for years. Rather than adding more tools or hiring more analysts to manage tool sprawl, organizations can deploy intelligent orchestration that maximizes the value of existing security investments while dramatically improving operational efficiency.
Despite strong adoption intentions, organizations face legitimate concerns about AI implementation. Data privacy and regulatory concerns top the list (24% of respondents), followed by integration difficulties (18%). However, these barriers are increasingly viewed as tactical challenges rather than strategic blockers, given the overwhelming consensus that AI represents the future of effective security operations.
Security leaders evaluating AI SOC solutions should prioritize coverage across alert types, accuracy in threat detection, investigation quality and transparency, workflow integration capabilities, time to value, and robust data privacy protections.
The security tool integration challenge won't be solved by adding more point solutions or building more custom APIs. Organizations need intelligent orchestration that can adapt to their unique tool combinations and evolving security requirements. As security threats become more sophisticated and attack surfaces continue expanding, the ability to rapidly correlate data across multiple security platforms becomes a competitive advantage.
AI orchestration transforms what was once an operational burden into a strategic capability. Organizations can maintain their investments in specialized security tools while gaining the integration benefits that have remained elusive through traditional approaches. The integration nightmare has a solution that scales with modern threat landscapes while maximizing return on existing security infrastructure investments.
The future of security operations lies not in replacing human expertise, but in amplifying it through intelligent orchestration that only artificial intelligence can provide.
Security tool sprawl in the SOC refers to deploying dozens of point solutions that each expose different data, queries, and timestamps. Security tool sprawl in the SOC creates silos that slow investigations and hide relationships that span email, identity, endpoint, network, and cloud.
Tool sprawl impacts alert triage and investigation by forcing analysts to pivot across SIEM, EDR, IAM, email security, threat intelligence, and case tools. Tool sprawl impacts alert triage and investigation through context switching and manual correlation that can turn a 10 minute task into an hour.
The difference between SOAR and an AI SOC orchestrator is that SOAR automates predefined playbooks while an AI SOC orchestrator adapts decisions during investigations. The difference between SOAR and an AI SOC orchestrator is that SOAR needs heavy maintenance and struggles with novel patterns, while an AI SOC orchestrator learns which tools and queries to run for each alert.
An AI SOC orchestrator works with SIEM, EDR, and identity systems by understanding each system’s context and capabilities and querying them in parallel. An AI SOC orchestrator works with SIEM, EDR, and identity systems to correlate evidence in real time and present a unified view for investigation.
AI SOC orchestration can deliver measurable improvements in a SOC such as reducing Mean Time to Investigate from over 25 minutes to 3 to 4 minutes in reported cases helping teams achieve 24/7 coverage without the additional headcount.
An AI SOC orchestrator normalizes data across different tools by translating naming, timestamps, and schemas into a common model to create a coherent timeline that spans email, endpoint, network, cloud, and identity.
SOC managers should evaluate AI SOC solutions by looking at coverage across alert types, accuracy, investigation quality and transparency, workflow integration, time to value, and data privacy posture. SOC managers should evaluate AI SOC solutions by verifying integration depth with SIEM, SOAR, EDR, identity, and case tools, as well as data normalization and detection engineering support.
Download to learn what’s driving AI adoption in the SOC, straight from 300+ CISOs and SOC leaders.
.png)
Ajmal Kohgadai
.svg)
As the Director of Product Marketing at Prophet Security, Ajmal drives marketing and growth strategies and helps security professionals see how AI is transforming security operations.
-min.avif)
-min.avif)
